Qwen3 32B (Groq) vs Sonar: API Cost Comparison
Compare the API pricing, context windows, features, and real-world cost projections for Qwen3 32B (Groq) (Groq) and Sonar (Perplexity). Use the interactive calculator below to compute your exact monthly cost based on your token usage and request volume.
Prices verified Mar 10, 2026
Interactive Cost Calculator
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Showing costs for 2 models. Cheapest: Qwen3 32B (Groq) at $5.85/month.
| Alert | |||||||
|---|---|---|---|---|---|---|---|
BestQwen3 32B (Groq) | Groq | $5.85 | $0.000585 | $2.90 | $2.95 | 61.0% | Alerts coming soon |
Sonar | Perplexity | $15.00 | $0.001500 | $10.00 | $5.00 | — | Alerts coming soon |
Monthly Cost Comparison
Price Comparison at a Glance
All prices are in USD per 1 million tokens ($/M tokens). Lower is cheaper.
| Pricing Type | Qwen3 32B (Groq) | Sonar | Cheaper |
|---|---|---|---|
| Input (standard) | $0.29/M | $1.00/M | Qwen3 32B (Groq) |
| Output | $0.59/M | $1.00/M | Qwen3 32B (Groq) |
Prices last verified: 2026-03-06 – 2026-03-10
Cost Breakdown by Usage Volume
Estimated monthly costs at different request volumes, assuming 1,000 input tokens and 500 output tokens per request. Adjust in the calculator above for your specific use case.
| Volume | Qwen3 32B (Groq)Monthly | SonarMonthly | Qwen3 32B (Groq)Per request | SonarPer request |
|---|---|---|---|---|
| 1K requests/mo | $0.58 | $1.50 | $0.000585 | $0.001500 |
| 10K requests/mo | $5.85 | $15.00 | $0.000585 | $0.001500 |
| 100K requests/mo | $58.50 | $150.00 | $0.000585 | $0.001500 |
| 1M requests/mo | $585.00 | $1,500.00 | $0.000585 | $0.001500 |
Green values indicate the lower-cost option at each volume tier. Cost per request is calculated at 1,000 input + 500 output tokens using standard (non-batch, non-cached) pricing.
Price History
Input price per 1M tokensWhen to Choose Qwen3 32B (Groq)
by Groq
- General chatbot
- Code generation
Input / 1M tokens
$0.29/M
Output / 1M tokens
$0.59/M
Context window
131,072
Tier
mid
When to Choose Sonar
by Perplexity
- RAG / Semantic search
- General chatbot
Input / 1M tokens
$1.00/M
Output / 1M tokens
$1.00/M
Context window
127,072
Tier
budget
Key Differences Beyond Price
Cost is only one factor in choosing an AI model. Context window size, rate limits, supported features, and latency all affect whether a model fits your use case.
| Capability | Qwen3 32B (Groq) | Sonar |
|---|---|---|
| Context window | 131,072 tokens | 127,072 tokens |
| Max output tokens | 8,192 tokens | 8,192 tokens |
| Performance tier | Mid | Budget |
| Vision / image input | No | No |
| Function calling | Yes | No |
| JSON mode | Yes | No |
| Prompt caching | No | No |
| Batch API (50% discount) | No | No |
| Extended reasoning | Yes | No |
| Fine-tuning | No | No |
| Rate limit (req/min) | 30 | Not published |
Qwen3 32B (Groq) notes
Qwen3 32B with reasoning capabilities on Groq hardware.
Sonar notes
Search pricing: $5-$12 per 1,000 requests depending on context size. Includes grounded real-time web search in every response.
Frequently Asked Questions
Is Qwen3 32B (Groq) cheaper than Sonar?
At standard usage (1,000 input tokens, 500 output tokens, 100,000 requests/month), Qwen3 32B (Groq) costs $58.50/month versus $150.00/month for Sonar — a 61% saving. Your actual savings will vary based on your token profile; output-heavy workloads amplify differences in output pricing.
Which model has a larger context window, Qwen3 32B (Groq) or Sonar?
Qwen3 32B (Groq) has a larger context window at 131,072 tokens, compared to 127,072 tokens for Sonar. A larger context window is important for processing long documents, multi-turn conversations, or large codebases without truncation.
Do Qwen3 32B (Groq) and Sonar support the Batch API?
Neither Qwen3 32B (Groq) nor Sonar currently supports a batch API with discounted pricing. For batch-eligible alternatives, consider models from OpenAI, Anthropic, or Google that include batch API support.
Which model offers better prompt caching?
Neither Qwen3 32B (Groq) nor Sonar currently supports prompt caching. For prompt-caching capable alternatives, consider Claude models from Anthropic or GPT-4o from OpenAI.
What are the best use cases for Qwen3 32B (Groq) vs Sonar?
Both models are well-suited for General chatbot. Qwen3 32B (Groq) is particularly strong for Code generation. Sonar is favored for RAG / Semantic search. At the same quality level, the lower-cost model is usually preferable; use this page's calculator to compare total monthly spend at your volume.
What is the cost per request for Qwen3 32B (Groq) vs Sonar?
At 1,000 input tokens and 500 output tokens per request — a typical conversational workload — Qwen3 32B (Groq) costs $0.000585 per request and Sonar costs $0.001500 per request. At 100,000 requests/month, that translates to $58.50 and $150.00 respectively. Use the interactive calculator to adjust these parameters for your actual workload.
Qwen3 32B (Groq) vs Sonar: Summary
When comparing Qwen3 32B (Groq) and Sonar for API cost, the right choice depends on your workload's token profile, required features, and tolerance for latency. Qwen3 32B (Groq) offers lower total cost at standard usage volumes (1,000 input + 500 output tokens per request at 100,000 requests/month) at $58.50/month, compared to $150.00/month for Sonar.
Both models are priced in USD per million tokens, the standard unit across all major AI API providers. Qwen3 32B (Groq) charges $0.29/M for input tokens and $0.59/M for output tokens. Sonar charges $1.00/M input and $1.00/M output. If your workload is output-heavy (more tokens generated than consumed as input), the model with the lower output price compounds cost savings significantly at scale.
Context window capacity differs between the two: Qwen3 32B (Groq) supports up to 131,072 tokens in a single request, versus 127,072 tokens for Sonar. A larger context window is essential for document summarization, large codebase analysis, and multi-document retrieval-augmented generation (RAG) applications.
Use the interactive calculator at the top of this page to enter your actual token usage and monthly request volume for a precise cost comparison tailored to your workload. Adjust for batch API discounts and prompt caching to find the most cost-effective option for your specific deployment.
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